• multivariate statistical techniques;
  • Tigris River;
  • water quality;
  • water pollution


The Tigris is one of the most important transboundary rivers in western Asia and originates in the Toros mountains of the Eastern Anatolia region of Turkey. Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA), were applied for the evaluation of temporal/spatial variations and the interpretation of a water quality data set for the Tigris River, which was obtained during 1 year of monitoring. This study presents the usefulness of multivariate statistical techniques for the evaluation and interpretation of complex water quality data sets and apportionment of pollution sources/factors to obtain better information about water quality and the design of a monitoring network for the effective management of water resources. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified seven monitoring sites into three groups, that is, less polluted sites, medium polluted sites and highly polluted sites, based on similarities in the water quality characteristics. PCA/FA identified five factors in the data structure, which explained 77.5% of the total variance of the data set. This allowed us to group the selected parameters according to common features and to evaluate the influence of each group on the overall variation in water quality. Varifactors obtained from the factor analysis indicated that the parameters responsible for water quality variation were mainly related to soluble salts (natural), organic pollution and nutrients (anthropogenic). Copyright © 2011 John Wiley & Sons, Ltd.